Instance-based process matching using event-log information

Han Van Der Aa; Avigdor Gal; Henrik Leopold; Hajo A. Reijers; Tomer Sagi; Roee Shraga

doi:10.1007/978-3-319-59536-8_18

Instance-based process matching using event-log information

Han Van Der Aa, Avigdor Gal, Henrik Leopold, Hajo A. Reijers, Tomer Sagi, Roee Shraga

Technion - Israel Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Process model matching provides the basis for many process analysis techniques such as inconsistency detection and process querying. The matching task refers to the automatic identification of correspondences between activities in two process models. Numerous techniques have been developed for this purpose, all share a focus on process-level information. In this paper we introduce instance-based process matching, which specifically focuses on information related to instances of a process. In particular, we introduce six similarity metrics that each use a different type of instance information stored in the event logs associated with processes. The proposed metrics can be used as standalone matching techniques or to complement existing process model matching techniques. A quantitative evaluation on real-world data demonstrates that the use of information from event logs is essential in identifying a considerable amount of correspondences.

Original language	American English
Title of host publication	Advanced Information Systems Engineering - 29th International Conference, CAiSE 2017
Editors	Eric Dubois, Klaus Pohl
Publisher	Springer Verlag
Pages	283-297
Number of pages	15
Volume	10253
ISBN (Print)	9783319595351, 978-3-319-59535-1
DOIs	https://doi.org/10.1007/978-3-319-59536-8_18
State	Published - 2017
Event	Forum and Doctoral Consortium Papers Presented at the 29th International Conference on Advanced Information Systems Engineering, CAiSE-Forum-DC 2017 - Essen, Germany Duration: 12 Jun 2017 → 16 Jun 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10253 LNCS

Conference

Conference	Forum and Doctoral Consortium Papers Presented at the 29th International Conference on Advanced Information Systems Engineering, CAiSE-Forum-DC 2017
Country/Territory	Germany
City	Essen
Period	12/06/17 → 16/06/17

Keywords

Event logs
Process model matching
Process similarity

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-59536-8_18

Cite this

Van Der Aa, H., Gal, A., Leopold, H., Reijers, H. A., Sagi, T., & Shraga, R. (2017). Instance-based process matching using event-log information. In E. Dubois, & K. Pohl (Eds.), Advanced Information Systems Engineering - 29th International Conference, CAiSE 2017 (Vol. 10253, pp. 283-297). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10253 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-59536-8_18

Van Der Aa, Han ; Gal, Avigdor ; Leopold, Henrik et al. / Instance-based process matching using event-log information. Advanced Information Systems Engineering - 29th International Conference, CAiSE 2017. editor / Eric Dubois ; Klaus Pohl. Vol. 10253 Springer Verlag, 2017. pp. 283-297 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Process model matching provides the basis for many process analysis techniques such as inconsistency detection and process querying. The matching task refers to the automatic identification of correspondences between activities in two process models. Numerous techniques have been developed for this purpose, all share a focus on process-level information. In this paper we introduce instance-based process matching, which specifically focuses on information related to instances of a process. In particular, we introduce six similarity metrics that each use a different type of instance information stored in the event logs associated with processes. The proposed metrics can be used as standalone matching techniques or to complement existing process model matching techniques. A quantitative evaluation on real-world data demonstrates that the use of information from event logs is essential in identifying a considerable amount of correspondences.",

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Van Der Aa, H, Gal, A, Leopold, H, Reijers, HA, Sagi, T & Shraga, R 2017, Instance-based process matching using event-log information. in E Dubois & K Pohl (eds), Advanced Information Systems Engineering - 29th International Conference, CAiSE 2017. vol. 10253, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10253 LNCS, Springer Verlag, pp. 283-297, Forum and Doctoral Consortium Papers Presented at the 29th International Conference on Advanced Information Systems Engineering, CAiSE-Forum-DC 2017, Essen, Germany, 12/06/17. https://doi.org/10.1007/978-3-319-59536-8_18

Instance-based process matching using event-log information. / Van Der Aa, Han; Gal, Avigdor; Leopold, Henrik et al.
Advanced Information Systems Engineering - 29th International Conference, CAiSE 2017. ed. / Eric Dubois; Klaus Pohl. Vol. 10253 Springer Verlag, 2017. p. 283-297 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10253 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Reijers, Hajo A.

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AU - Shraga, Roee

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

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N2 - Process model matching provides the basis for many process analysis techniques such as inconsistency detection and process querying. The matching task refers to the automatic identification of correspondences between activities in two process models. Numerous techniques have been developed for this purpose, all share a focus on process-level information. In this paper we introduce instance-based process matching, which specifically focuses on information related to instances of a process. In particular, we introduce six similarity metrics that each use a different type of instance information stored in the event logs associated with processes. The proposed metrics can be used as standalone matching techniques or to complement existing process model matching techniques. A quantitative evaluation on real-world data demonstrates that the use of information from event logs is essential in identifying a considerable amount of correspondences.

AB - Process model matching provides the basis for many process analysis techniques such as inconsistency detection and process querying. The matching task refers to the automatic identification of correspondences between activities in two process models. Numerous techniques have been developed for this purpose, all share a focus on process-level information. In this paper we introduce instance-based process matching, which specifically focuses on information related to instances of a process. In particular, we introduce six similarity metrics that each use a different type of instance information stored in the event logs associated with processes. The proposed metrics can be used as standalone matching techniques or to complement existing process model matching techniques. A quantitative evaluation on real-world data demonstrates that the use of information from event logs is essential in identifying a considerable amount of correspondences.

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Van Der Aa H, Gal A, Leopold H, Reijers HA, Sagi T, Shraga R. Instance-based process matching using event-log information. In Dubois E, Pohl K, editors, Advanced Information Systems Engineering - 29th International Conference, CAiSE 2017. Vol. 10253. Springer Verlag. 2017. p. 283-297. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-59536-8_18

Instance-based process matching using event-log information

Abstract

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Conference

Keywords

All Science Journal Classification (ASJC) codes

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